Originally, the Ant Algorithms are used to solve discrete and combinatorial optimization problems. Various extensions of Ant Colony Optimization (ACO) are proposed to deal with optimization problems, defined in continuous domains. One of the most useful algorithms of this type, is ACOR, the Ant Colony Optimization for Continuous Domains, proposed by Socha and Dorigo, in 2008 (here). ACOR is an ...

Ant Colony Optimization (ACO) are a set of probabilistic metaheuristics and an intelligent optimization algorithms, inspired by social behavior of ants. ACO algorithms are also categorized as Swarm Intelligence methods, because of implementation of this paradigm, via simulation of ants behavior in the structure of these algorithms. First ACO algorithm is proposed by Marco Dorigo in his PhD thesis, in ...

Particle Swarm Optimization (PSO) is an intelligent optimization algorithm based on the Swarm Intelligence. It is based on a simple mathematical model, developed by Kennedy and Eberhart in 1995, to describe the social behavior of birds and fish. The model relies mostly on the basic principles of self-organization which is used to describe the dynamics of complex systems. Swarm intelligence ...

Genetic Algorithms (GAs) are most famous Evolutionary Algorithms (EAs) which are inspired from natural evolution and selection. Their main application is in the field of optimization. Hence they are applicable to any kind of problem, which can be converted or stated as an optimization task. To read more about Genetic Algorithms, you can see the related article in Wikipedia (here). ...